A Fully Automated and Explainable Algorithm for the Prediction of Malignant Transformation in Oral Epithelial Dysplasia

Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. Its grading suffers from significant inter-/intra- observer variability, and does not reliably predict malignancy progression, potentially leading to suboptimal treatment decisions. To address this, we developed a novel artificial intelligence algorithm that can assign an Oral Malignant Transformation (OMT) risk score, based on histological patterns in the in Haematoxylin and Eosin stained whole slide images, to quantify the risk of OED progression. The algorithm is based on the detection and segmentation of nuclei within (and around) the epithelium using an in-house segmentation model. We then employed a shallow neural network fed with interpretable morphological/spatial features, emulating histological markers. We conducted internal cross-validation on our development cohort (Sheffield; n = 193 cases) followed by independent validation on two external cohorts (Birmingham and Belfast; n = 92 cases). The proposed OMTscore yields an AUROC = 0.74 in predicting whether an OED progresses to malignancy or not. Survival analyses showed the prognostic value of our OMTscore for predicting malignancy transformation, when compared to the manually-assigned WHO and binary grades. Analysis of the correctly predicted cases elucidated the presence of peri-epithelial and epithelium-infiltrating lymphocytes in the most predictive patches of cases that transformed (p < 0.0001). This is the first study to propose a completely automated algorithm for predicting OED transformation based on interpretable nuclear features, whilst being validated on external datasets. The algorithm shows better-than-human-level performance for prediction of OED malignant transformation and offers a promising solution to the challenges of grading OED in routine clinical practice

Transformer-based Model for Oral Epithelial Dysplasia Segmentation

Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. OED grading is subject to large inter/intra-rater variability, resulting in the under/over-treatment of patients. We developed a new Transformer-based pipeline to improve detection and segmentation of OED in haematoxylin and eosin (H&amp;E) stained whole slide images (WSIs). Our model was trained on OED cases (n = 260) and controls (n = 105) collected using three different scanners, and validated on test data from three external centres in the United Kingdom and Brazil (n = 78). Our internal experiments yield a mean F1-score of 0.81 for OED segmentation, which reduced slightly to 0.71 on external testing, showing good generalisability, and gaining state-of-the-art results. This is the first externally validated study to use Transformers for segmentation in precancerous histology images. Our publicly available model shows great promise to be the first step of a fully-integrated pipeline, allowing earlier and more efficient OED diagnosis, ultimately benefiting patient outcomes

Proceedings of the 3rd Biennial Conference of the Society for Implementation Research Collaboration (SIRC) 2015: advancing efficient methodologies through community partnerships and team science

It is well documented that the majority of adults, children and families in need of evidence-based behavioral health interventionsi do not receive them [1, 2] and that few robust empirically supported methods for implementing evidence-based practices (EBPs) exist. The Society for Implementation Research Collaboration (SIRC) represents a burgeoning effort to advance the innovation and rigor of implementation research and is uniquely focused on bringing together researchers and stakeholders committed to evaluating the implementation of complex evidence-based behavioral health interventions. Through its diverse activities and membership, SIRC aims to foster the promise of implementation research to better serve the behavioral health needs of the population by identifying rigorous, relevant, and efficient strategies that successfully transfer scientific evidence to clinical knowledge for use in real world settings [3]. SIRC began as a National Institute of Mental Health (NIMH)-funded conference series in 2010 (previously titled the “Seattle Implementation Research Conference”; $150,000 USD for 3 conferences in 2011, 2013, and 2015) with the recognition that there were multiple researchers and stakeholdersi working in parallel on innovative implementation science projects in behavioral health, but that formal channels for communicating and collaborating with one another were relatively unavailable. There was a significant need for a forum within which implementation researchers and stakeholders could learn from one another, refine approaches to science and practice, and develop an implementation research agenda using common measures, methods, and research principles to improve both the frequency and quality with which behavioral health treatment implementation is evaluated. SIRC’s membership growth is a testament to this identified need with more than 1000 members from 2011 to the present.ii SIRC’s primary objectives are to: (1) foster communication and collaboration across diverse groups, including implementation researchers, intermediariesi, as well as community stakeholders (SIRC uses the term “EBP champions” for these groups) – and to do so across multiple career levels (e.g., students, early career faculty, established investigators); and (2) enhance and disseminate rigorous measures and methodologies for implementing EBPs and evaluating EBP implementation efforts. These objectives are well aligned with Glasgow and colleagues’ [4] five core tenets deemed critical for advancing implementation science: collaboration, efficiency and speed, rigor and relevance, improved capacity, and cumulative knowledge. SIRC advances these objectives and tenets through in-person conferences, which bring together multidisciplinary implementation researchers and those implementing evidence-based behavioral health interventions in the community to share their work and create professional connections and collaborations

Aquila audax sample locations

Sample locations for 154 Aquila audax tissues in Tasmania

Aquila audax microsatellite data

Data for 20 microsatellite loci Tasmanian Aquila audax, in genepop format

Data from: Intrinsic factors drive spatial genetic variation in a highly vagile species, the wedge-tailed eagle (Aquila audax), in Tasmania

Knowledge of dispersal in a species, both its quantity and the factors influencing it, are crucial for our understanding of ecology and evolution, and for species conservation. Here we quantified and formally assessed the potential contribution of extrinsic factors on individual dispersal in the threatened Tasmanian population of wedge-tailed eagle, Aquila audax. As successful breeding by these individuals appears strongly related to habitat, we tested the effect of landscape around sampling sites on genetic diversity and spatial genetic variation, as these are influenced by patterns of dispersal. Similarly, we also tested whether habitat intervening sampling sites could explain spatial genetic variation. Twenty microsatellites were scored, but only a small proportion of spatial genetic variation (4.6%) could be explained by extrinsic factors, namely habitat suitability and elevation between sites. However, significant clinal genetic variation was evident across Tasmania, which we explain by intrinsic factors, likely high natal philopatry and occasional long-distance dispersal. This study demonstrates that spatial genetic variation can be detected in highly vagile species at spatial scales that are small relative to putative dispersal ability, although here there was no substantial relationship with landscape factors tested

Forest specialist species in the urban landscape: Do different levels of urbanization affect the movements of Forest Red-tailed Black Cockatoos (Calyptorhynchus banksii naso)?

Anthropogenic landscape modification which leads to the displacement of species, is arguably one of the most profound impacts on animal movement globally. In urban landscapes, animal movement is generally impacted by varying levels of increased urbanization. However, this is species dependent and is mostly guided by the surrounding habitat. Fragmentation and habitat patch isolation must be considered at scales appropriate to the study species. Using telemetry, we test these assumptions investigating movement patterns of flocks of Forest Red-tailed Black Cockatoos (Calyptorhynchus banksii naso; RTBC) between three regions: urban, peri-urban, and forest using GPS and satellite PTT. This species occurs at varying levels of urbanization, however, how this might affect its movements is largely unknown. We did not find evidence that RTBC movement was impaired in the urban region compared with peri-urban or forest regions. It found, however, a significant within-region variation in movement extent among flocks and across regions depending on foraging resource availability and location. Differences in daily movement distance (Av. 4.96 - 16.41 km) and home range size (6.02 - 52.57 km2) between urban flocks appeared to be associated with the proximity of green spaces as roosts and foraging sites, with roadside vegetation providing important foraging resources and movement corridors. Key urban habitats were predominantly located in public nature reserves and private properties, with roadside vegetation connecting these sites for RTBC. The findings of this study highlight that conservation management for this and many other threatened species should regard the urban landscape as a critical habitat for urban adapted species. This would include management of its green spaces with connectivity and offsets from roads in mind. Furthermore, future research should focus on identifying additional key habitat sites (resource selection) and species distribution modeling, which will facilitate an active and adaptive approach towards this species' conservation management